The Seeker's Dilemma: Realistic Formulation and Benchmarking for Hardware Trojan Detection
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Abstract (arXiv)
This work focuses on advancing security research in the hardware design space by formally defining the realistic problem of Hardware Trojan (HT) detection. The goal is to model HT detection more closely to the real world, i.e., describing the problem as "The Seeker's Dilemma" (an extension of Hide&Seek on a graph), where a detecting agent is unaware of whether circuits are infected by HTs or not. Using this theoretical problem formulation, we create a benchmark that consists of a mixture of HT-free and HT-infected restructured circuits while preserving their original functionalities. The restructured circuits are randomly infected by HTs, causing a situation where the defender is uncertain if a circuit is infected or not. We believe that our innovative dataset will help the community better judge the detection quality of different methods by comparing their success rates in circuit classification. We use our developed benchmark to evaluate three state-of-the-art HT detection tools to show baseline results for this approach. We use Principal Component Analysis to assess the strength of our benchmark, where we observe that some restructured HT-infected circuits are mapped closely to HT-free circuits, leading to significant label misclassification by detectors.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 61 references that are in the map. First posted on arXiv 27 Feb 2024.
Builds on
8 of 61 references.
References · 8
- 2021OpenABC-D: A Large-Scale Dataset For Machine Learning Guided Integrated Circuit Synthesiscited by 30
- 2021HW2VEC: A Graph Learning Tool for Automating Hardware Securitycited by 16
- 2022ATTRITION: Attacking Static Hardware Trojan Detection Techniques Using Reinforcement Learningcited by 11
- 2022Hardware Trojan Insertion Using Reinforcement Learningcited by 6
- 2022DETERRENT: Detecting Trojans using Reinforcement Learningcited by 5
- 2023Trojan Playground: A Reinforcement Learning Framework for Hardware Trojan Insertion and Detectioncited by 4
- 2023Multi-criteria Hardware Trojan Detection: A Reinforcement Learning Approachcited by 2
- 2017Hardware Trojan Detection Game: A Prospect-Theoretic Approachcited by 1
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Citing papers per year: 2024: 1.
1 paper.